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fpca.pred<-function(fpcs, muhat,eigenfuncs){
## predicted trajectory for each sample curve
##para: fpcs: (estimated) FPC score; (returned by fpca.score)
## muhat, eigenfuncs: (estimated) mean and eigenfunctions evaluated on a fine grid. (returned by fpca.mle)
##return: predicted trajectories: grid_length by n
eigenfuncs.u<-t(eigenfuncs) ## dimmension: grid_length by K
result<-muhat+eigenfuncs.u%*%t(fpcs)
return(result) ##each column corresponds to a predicted trajectory
}
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